(energetic music) >> Hi, and welcome to another Cube conversation, where we go in-depth with the thought leaders in the industry that are making significant changes to how we conduct digital business and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics. But, they're also warning that the value of their analytics systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information, that the business has been using to run the business on for years. Now, we've looked at that as silos and maybe it is. Although, partly, that's in response to the need to have good policy, good governance, and good certainty and practicably in how the system behaves and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendu Yogurtcu is the CTO of Syncsort. Tendu, welcome back to The Cube! >> Hi Peter. It's great to be back here in The Cube. >> Excellent! So, look, let's start with the, let's start with a quick update on Syncsort. How are you doing, what's going on? >> Oh, it's been really exciting time at Syncsort. We have seen a tremendous growth in the last three years. We quadrupled our revenue, and also number of employees, through both organic innovation and growth, as well as through acquisitions. So, we now have 7,000 plus customers in over 100 countries, and, we still have the eight 40 Fortune 100, serving large enterprises. It's been a really great journey. >> Well, so, let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019 rather, 2019 was going to be the year that the enterprise assert itself in the cloud. We had seen a lot of developers drive cloud forward. We've seen a lot of analytics drive cloud forward. But, now as enterprises are entering into cloud in a big way, they're generating, or bringing with them, new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data. Data is the core. And, take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations, starting with the data lake. Everybody started creating the centralized data hub, enabling advanced analytics and creating a data marketplace for their internal, or external clients. And, the early data lakes were for utilizing Hadoop on premise architectures. Now, we are also seeing data lakes, sometimes, expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around, once I create this data marketplace, the access to the data, critical customer data, critical product data, >> Order data. >> Order data, is a bigger challenge than I thought that it would be in the pilot project. Because, these critical data sets, and core data sets, often in financial services, banking and insurance, and health care, are in environments, data platforms that these companies have invested over multiple decades. And, I'm not referring to that as legacy because definition of legacy changes. These environment's platforms have been holding this current critical data assets for decades successfully. So-- >> We call them high-value traditional applications. >> High-valude traditional sounds great. >> Because, they're traditional. We know what they do, and there's a certain operational certainty, and we've built up the organization around them to take care of those assets. >> But, they still are very very high-value. >> Exactly. And, making those applications and data available for next generation, next wave platforms, is becoming a challenge, for couple of different reasons. One, accessing this data. And, accessing this data, making sure the policies and the security, and the privacy around these data stores are preserved when the data is available for advanced analytics. Whether it's in the cloud or on premise deployments. >> So, before we go to the second one, I want to make sure I'm understanding that, because it seems very very important. >> Yes. >> That, what you're saying is, if I may, the data is not just the ones and the zeroes in the file. The data really start, needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data's getting used. >> Absolutely. And, there are challenges around that, because now you have to have skill sets to understand the data in those different types of stores. Relational data warehouses. Mainframe, IBMI, SQL, Oracle. Many different data owners, and different teams in the organization. And, then, you have to make sense of it and preserve the policies around each of these data assets, while bringing it to the new analytics environments. And, make sure that everybody's aligned with the access to privacy, and the policies, and the governance around that data. And also, mapping to metadata, to the target systems, right? That's a big challenge, because somebody who understands these data sets in a mainframe environment is not necessarily understanding the cloud data stores or the new data formats. So, how do you, kind of, bridge that gap, and map into the target-- >> And, vice-versa, right? >> Yes. >> So. >> Likewise, yes. >> So, this is where Syncsort starts getting really interesting. Because, as you noted, a lot of the folks in the mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint. >> Yes. >> And, a lot of the folks in the cloud that have been doing things with object stores and whatnot, may not, and Hadoop, may not have the knowledge of how the mainframe works. And, so, those two sides are seeing silos, but, the reality is, both sides have set up policies and governance models, and security regimes, and everything else, because it works for the workloads that are in place on each side. So, Syncsort's an interesting company, because, you guys have experience of crossing that divide. >> Absolutely. And, we see both the next phase, and the existing data platforms, as a moving, evolving target. Because, these challenges have existed 20 years ago, 10 years ago. It's just the platforms were different. The volume, the variety, complexity was different. However, Hadoop, five, ten years ago, was the next wave. Now, it's the cloud. Blockchain will be the next platform that we have to, still, kind of, adopt and make sure that we are advancing our data and creating value out of data. So, that's, accessing and preserving those policies is one challenge. And, then, the second challenge is that as you are making these data sets available for analytics, or machine learning, data science applications, deduplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge. Because, if you train the models with the bad data, if you create the models with the bad data, you have bad model, and then bad data inside. So, machine learning and artificial intelligence depends on the data, and the quality of the data. So, it's not just bringing all enterprise data for analytics. It's also making sure that the data is delivered in a trusted way. That's the big challenge. >> Yeah. Let me build on that, if I may, Tendu. Because, a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So, you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, your only real visibility into the quality of the outcome of these tools is visibility into the quality of the data that's going into the building of these models. >> Correct. >> Have I got that right? >> Correct. And, in machine learning, the effect of bad data is, really, it multiplies. Because of the training of the model, as well as insights. And, with Blockchain, in the future, it will also become very critical because, once you load the data into Blockchain platform, it's immutable. So, data quality comes at a higher price, in some sense. That's another big challenge. >> Which is to say, that if you load bad data into a Blockchain, it's bad forever. >> Yes. That's very true. So, that's, obviously, another area that Syncsort, as we are accessing all of the enterprise data, delivering high-quality data, discovering and understanding the data, and delivering the duplicated standardized enriched data to the machine learning and AI pipeline, and analytics pipeline, is an area that we are focused with our products. And, a third challenge is that, as you are doing it, the speed starts mattering. Because, okay, I created the data lake or the data hub. The next big use case we started seeing is that, "Oh yeah, but I have 20 terabyte data, "only 10% is changing on a nightly basis. "So, how do I keep my data lake in sync? "Not only that, I want to keep my data lake in sync, "I also would like to feed that change data "and keep my downstream applications in sync. "I want to feed the change data to the microservices "in the cloud." That speed of delivery started really becoming a very critical requirement for the business. >> Speed, and the targeting of the delivery. >> Speed of the targeting, exactly. Because, I think the bottom line is, you really want to create an architecture that you can be agnostic. And, also be able to deliver at the speed the business is going to require at different times. Sometimes, it's near real-time, and at batch, sometimes it's real-time, and you have to feed the changes as quickly as possible to the consumer applications and the microservices in the cloud. >> Well, we've got a lot of CIO's who are starting to ask us questions about, especially, since they start thinking about Kubernetes, and Istio, and other types of platforms that are intended to facilitate the orchestration, and ultimately, the management of how these container-based applications work. And, we're starting to talk more about the idea of data assurance. Make sure the data's good. Make sure it's been high-quality. Make sure it's being taken care of. But, also make sure that it's targeted where it needs to be. Because, you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes. But, you haven't made the data available to that Kubernetes-based application, so that it can, actually, run. And, a lot of CIO's, and a lot of application development leaders, and a lot of business people, are now starting to think about that. "How do I make sure the data is where it needs to be, "so that the applications run when they need to run?" >> That's a great point. And, going back to your, kind of, comment around cloud, and taking advantage of cloud architectures. One of the things we have observed is organizations, for sure, looking at cloud, in terms of scalability, elasticity, and reducing costs. They did lift and shift of applications. And, not all applications can be taking advantage of cloud elasticity, then you do that. Most of these applications are created for the existing on-premise fixed architectures. So, they are not designed to take advantage of that. And, we are seeing a shift now. And, the shift is around, instead of, trying to, kind of, lift and shift existing applications. One, for new applications, let me try and adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor-agnostic, for cloud vendors. But, more importantly, let me try to have some best practices in the organization. The new applications can be created to take advantage of the elasticity. Even though, they may not be running in the cloud yet. So, some organizations refer to this as cloud native, cloud first, some different terms. And, make the data. Because, the core asset here, is always the data. Make the data available, instead of going after the applications. Make the data from these existing on-premise and different platforms available for cloud. We are definitely seeing that the shift. >> Yeah, and make sure that it, and assure, that that data is high-quality, carries the policies, carries the governance, doesn't break in security models, all those other things. >> That is a big difference between how, actually, organizations ran into their Hadoop data lake implementations, versus the cloud architectures now. Because, when initial Hadoop data lake implementations happened, it was dump all the data. And, then, "Oh, I have to deal with the data quality now." >> It was also, "Oh, those mainframe people just would, "they're so difficult to work with." Meanwhile, you're still closing the books on a monthly basis, on a quarterly basis. You're not losing orders. Your customers aren't calling you on the phone angry. And, that, at the end of the day, is what a business has to do. You have to be able to extend what you can currently do, with a digital business approach. And, if you can replace certain elements of it, okay. But, you can't end up with less functionality as you move forward in the cloud. >> Absolutely. And, it's not just mainframe. It's IBMI, it's the Oracle, it's the teledata, it's the TDZa. It's growing rapidly, in terms of the complex stuff, that data infrastructure. And, for cloud, we are seeing now, a lot of pilots are happening with the cloud data warehouses. And, trying to see if the cloud data warehouses can accommodate some of these hybrid deployments. And, also, we are seeing, there's more focus, not after the fact, but, more focus on data quality from day one. "How am I going to ensure that "I'm delivering trusted data, and populating "the cloud data stores, or delivering trusted data "to microservices in the cloud?" There's greater focus for both governance and quality. >> So, high-quality data movement, that leads to high-quality data delivery, in ways that the business can be certain that whatever derivative work is done remains high-quality. >> Absolutely. >> Tendu Yogurtcu, thank you very much for being, once again, on The Cube. It's always great to have you here. >> Thank you Peter. It's wonderful to be here! >> Tandu Yogurtcu's the CTO of Syncsort, and once again, I want to thank you very much, for participating in this cloud, or this Cube conversation. Cloud on the mind, this Cube conversation. Until next time. (upbeat electronic music)